Systems exist that use simple dimensions to identify books that are similar. For example, the systems can (1) analyze item purchase histories and item viewing histories for a large number of users, (2) compare catalog or bibliographic data (e.g., author, subject, title, etc.) to look for titles with similar attributes, and/or (3) apply traditional collaborative filtering to users' ratings of individual books. However, these existing systems may have trouble identifying similar books when the books are self-published. The number of available self-published books may be large, yet only a small number of users may have viewed or read any given self-published book. Thus, few, if any, self-published books may be rated by enough users to provide meaningful recommendations.